La evolución de los insights desde la escucha social a la lectura por imagen: El caso L’Oreal

Abstract

Purpose: Show how L’Oréal Spain evolved from a classic social listening practice, to image driven insights by the usage of AI tools; the first step to correctly understand the vast amount of personal information, interests and desires that millions of consumers share every day in social networks. Design / Methodology / Approach: Hybrid approach, combining AI to quickly categorize, filter and run a preliminary analysis on images, with a manual coding of specific elements about the beauty industry. This procedure achieved two significant goals: First-hand learnings that could be directly used by marketing teams, A deep coded beauty image database that could be used to train AI algorithms in the future for a more powerful, automatized tool. Results: There are seven main tribes within the makeup universe in Spain, based on their techniques and products used. Furthermore, its evolution was also traced, and measured within customers, influencers and brands, to detect where a ‘sweet spot’ was taking place: a rising trend that was growing fast among consumers and influencers, but where brands were not yet present. Limitations / Implications: The main limitations of the study are two. On the one hand, the representativeness of Instagram for the Spanish beauty consumer. It was necessary to weight the different segments of the sample to reach the right, balanced conclusions. And on the other, AI algorithms capacity to code and filter images. At a certain stage, it was necessary to introduce human help to validate and perform a deeper coding. Contribution: Setting the first milestone on social image insights for the beauty industry, advancing from a purely text-based insight practice to a much richer information based on the looks that consumers share every day.